VR Teleoperation Framework for Multi-Robot AI Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems and AI technologies face challenges in fulfilling manufacturing jobs due to complexity and variability, leading to a skills gap and unemployment issues, particularly among low-skill workers.
Innovation Solution
A scalable teleoperation system leveraging low-skill workers' gaming skills, utilizing virtual reality to remotely control robots through a multi-user, multi-robot framework, combining user input with AI-generated control information via a master-apprentice framework, allowing efficient task completion and adaptation to new contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic systems and AI are used to fulfill manufacturing jobs, then productivity and automation are improved, but the systems cannot handle the complexity and variability of manufacturing contexts
Solution Approach 1:
The patent merges AI automation with human teleoperation to create a hybrid system. The AI handles routine tasks while human operators intervene for complex or variable situations through virtual reality interfaces, combining the strengths of both automation and human adaptability.
Solution Approach 2:
The patent introduces virtual reality control rooms and virtual agents as intermediaries between human operators and robotic systems. This intermediary layer enables human operators to effectively manage multiple robots and handle complex manufacturing contexts that pure AI cannot manage.
2Productivity
If pure AI automation is used, then productivity is improved, but the system lacks the adaptability to learn from human expertise in complex situations
Solution Approach 1:
The patent implements feedback loops where human operators provide corrections and guidance to AI systems through virtual reality interfaces. The AI learns from this human input and feedback, continuously improving its performance while maintaining high automation levels.
Solution Approach 2:
The patent uses virtual agents to prepare and pre-process information before human operators need to intervene. This preliminary action by AI assistants reduces the cognitive load on operators and enables more effective human-AI collaboration in complex situations.
3Adaptability or versatility
If human operators directly control robots, then adaptability to complex contexts is improved, but user time and operational efficiency decrease
Solution Approach 1:
The patent implements partial automation where AI handles routine and straightforward tasks autonomously, while human operators only intervene when necessary for complex situations. This partial action approach maintains adaptability while minimizing time loss by avoiding unnecessary human involvement in simple tasks.
Solution Approach 2:
The patent segments control tasks between AI and human operators based on complexity. Routine tasks are segmented to AI automation, while complex variable tasks are segmented to human operators, optimizing the division of labor to reduce overall time consumption while maintaining adaptability.
4Adaptability or versatility
If multiple users control multiple robots, then system versatility is improved, but coordination complexity and system management difficulty increase
Solution Approach 1:
The patent creates universal virtual control rooms that can manage multiple robot types and multiple users through a unified interface. This multi-functional virtual environment handles coordination automatically, reducing the complexity that would otherwise arise from managing multiple user-robot pairs directly.
Solution Approach 2:
The patent introduces virtual agents as intermediaries that automatically coordinate between multiple users and multiple robots. These virtual assistants manage the complexity of multi-user multi-robot coordination, allowing users to focus on their specific tasks while the system handles the coordination overhead.
Data Source
AI summary
In some aspects, a system comprises a computer hardware processor and a non-transitory computer-readable storage medium storing processor-executable instructions for receiving, from one or more sensors, sensor data relating to a robot; generating, using a statistical model, based on the sensor data, first control information for the robot to accomplish a task; transmitting, to the robot, the first control information for execution of the task; and receiving, from the robot, a result of execution of the task.